Duplicate from alecccdd/celeb-fbi

Co-authored-by: alec dd <alecccdd@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: height
+    dtype: float64
+  - name: weight
+    dtype: float64
+  - name: gender
+    dtype: int64
+  - name: age
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 1347895106
+    num_examples: 6487
+  - name: test
+    num_bytes: 162397956
+    num_examples: 721
+  download_size: 1587814198
+  dataset_size: 1510293062
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+license: apache-2.0
+task_categories:
+  - image-classification
+  - image-feature-extraction
+tags:
+  - human-body
+  - biometrics
+  - age-estimation
+  - height-estimation
+  - weight-estimation
+  - gender-classification
+  - celebrity
+---
+# Celeb-FBI: Celebrity Full Body Images Dataset
+
+A cleaned and restructured version of the Celeb-FBI dataset containing 7,208 full-body celebrity images with annotations for height, weight, age, and gender.
+
+## Dataset Description
+
+This dataset consists of worldwide celebrity images captured in standing, front-facing positions. It is designed for research on human attribute estimation from full-body images, including height, weight, age, and gender prediction tasks.
+
+### Dataset Structure
+
+```
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
+        num_rows: 6487
+    })
+    test: Dataset({
+        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
+        num_rows: 721
+    })
+})
+```
+
+### Features
+
+| Feature  | Type    | Description                                      |
+|----------|---------|--------------------------------------------------|
+| `id`     | int     | Unique identifier for the image                  |
+| `image`  | Image   | Full-body celebrity photograph                   |
+| `height` | float   | Height in centimeters (-1 if missing/invalid)    |
+| `weight` | float   | Weight in kilograms (-1 if missing/invalid)      |
+| `gender` | int     | 0 = Male, 1 = Female                             |
+| `age`    | int     | Age in years (-1 if missing/invalid)             |
+
+### Statistics
+
+| Attribute | Min   | Max    | Mean  | Valid Samples |
+|-----------|-------|--------|-------|---------------|
+| Height    | 79 cm | 259 cm | 170 cm | ~6,100       |
+| Weight    | 38 kg | 202 kg | 66 kg  | ~5,300       |
+| Age       | 14    | 97     | 42     | ~6,500       |
+| Gender    | —     | —      | 61% F  | 7,208        |
+
+## Data Processing
+
+This version of the dataset includes several improvements over the original:
+
+**Cleaning steps applied:**
+- Converted height from feet to centimeters for standardization
+- Removed implausible values (e.g., heights outside reasonable human range)
+- Missing or invalid values are encoded as `-1`
+- Fixed typos in original annotations
+- Manual corrections for identified mislabeled samples
+
+**Train/test split:**
+- Stratified 90/10 split based on height, age, weight buckets, and gender
+- Ensures balanced representation across attribute combinations
+
+**Note:** Approximately 14% of samples have at least one missing or invalid attribute value (marked as -1). The dataset contains some noise in annotations—users should account for this in their applications.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("alecccdd/celeb-fbi")
+
+# Access training data
+train_data = dataset["train"]
+
+# Example: iterate over samples
+for sample in train_data:
+    image = sample["image"]
+    height = sample["height"]  # in cm, -1 if missing
+    weight = sample["weight"]  # in kg, -1 if missing
+    gender = sample["gender"]  # 0=male, 1=female
+    age = sample["age"]        # -1 if missing
+    
+# Filter valid samples for a specific attribute
+valid_height_samples = train_data.filter(lambda x: x["height"] != -1)
+```
+
+## Intended Uses
+
+- Human attribute estimation research (height, weight, age, gender)
+- Multi-task learning on human body images
+- Benchmarking computer vision models for biometric prediction
+- Study of visual cues for physical attribute estimation
+
+## Limitations
+
+- Images are of celebrities and may not represent the general population
+- Annotation accuracy depends on publicly available biographical data
+- Some noise exists in the annotations; manual corrections were applied where identified but the dataset is not exhaustively verified
+- Limited age range representation at extremes (few samples under 20 or over 80)
+- Height and weight distributions may reflect celebrity demographics
+
+## Ethical Considerations
+
+This dataset uses publicly available images of celebrities. Users should be mindful of:
+- Privacy implications when developing attribute estimation systems
+- Potential biases in celebrity image datasets
+- Responsible use in downstream applications
+
+## Citation
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@misc{debnath2024celebfbibenchmarkdatasethuman,
+      title={Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach}, 
+      author={Pronay Debnath and Usafa Akther Rifa and Busra Kamal Rafa and Ali Haider Talukder Akib and Md. Aminur Rahman},
+      year={2024},
+      eprint={2407.03486},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.03486}, 
+}
+```
+
+**Paper:** [arXiv:2407.03486](https://arxiv.org/abs/2407.03486)

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